Dear Editor,
The use of global navigation satellite system (GNSS) technologies to study the hydrological cycle has gained increasing attention. Current research primarily spans two domains: GNSS hydrogeodesy and GNSS remote sensing. However, these areas remain fragmented within hydrology-related fields. While GNSS hydrogeodesy is limited in addressing hydrological applications, GNSS remote sensing extends into broader environmental domains. To bridge this gap, we propose the formalization of “GNSS hydrology,” an interdisciplinary field that integrates the principles of both GNSS hydrogeodesy and GNSS remote sensing, with a focus on the core capabilities of GNSS technologies in hydrology. We identify three distinct branches within GNSS hydrology—GNSS positioning, reflection, and transmission hydrology, collectively called GNSS-P.R.T. hydrology—and examine their technical roles and unique applications. The proposed concept offers a unifying framework for the broader hydrology research community, positioning it as a rapidly evolving field with vast potential.
Revisiting GNSS in cross-disciplinary hydrology research
The overarching objective of studying the global hydrological cycle is to observe, understand, and forecast the storage and movement of water across spatial and temporal scales under a changing climate. To achieve this, space-based measurements of water storage and fluxes are essential, covering scales ranging from local to continental/global and spanning various temporal resolutions. Moreover, space-based hydrological observations broadly fall into two key areas: hydrogeodesy and hydrological remote sensing.
Hydrogeodesy combines geodesy—concerned with the Earth’s shape, orientation, gravity field, and temporal variations—and hydrology.1 Satellite-based hydrogeodesy includes four primary technologies: altimetry, interferometric synthetic aperture radar (InSAR), gravimetry, and GNSS.2 The commonality among these methods is their ability to measure key hydrological variables such as surface water dynamics and/or total water storage (TWS) change, with the latter referring to the variation in the amount of water stored within a hydrological system over a specific period.
Hydrological remote sensing generally measures all types of water storage- and flux-related variables within the hydrological cycle, such as soil water, snow, ice, land water bodies, and specific atmospheric variables that closely interact with the land surface (e.g., precipitable water vapor [PWV]).3 For example, optical and SAR satellite imageries are commonly used to spatially represent hydrological variables, such as surface water extent or snow/ice cover. Passive microwave sensors, such as radiometers, are particularly effective for deriving storage-related variables, with L-band radiometers targeting soil moisture and C/X-band radiometers focused on snow and ice. Active microwave sensors like Ku/Ka-band radar are applied to surface/atmospheric profiling and precipitation measurement. A promising complement to active radar or passive radiometry is using existing, non-cooperative transmitters as illumination sources for bistatic radar. In this way, the current 100+ GNSS satellites transmitting L-band microwave signals are popular illumination sources that, with proper receiving platforms and receivers, offer a cost-effective complement to existing hydrological observation systems.
The role of GNSS: from the background above, GNSS has emerged as a versatile tool for measuring hydrological variables by leveraging geodetic and remote sensing approaches. Two key features of GNSS underpin this capability. First, its primary positioning function yields a highly accurate time series of vertical and horizontal displacements of the solid Earth, which can be linked to hydrological loading. This field, commonly called GNSS hydrogeodesy,4 emerged around the year 2000 and belongs to the discipline of hydrogeodesy, with a focus on monitoring the TWS change. Second, GNSS satellites transmit 1–2 GHz L-band signals, which were initially chosen for positioning due to their ability to work in clouds, snow, and rain. However, atmospheric and land surface errors—such as tropospheric delay and the land surface multipath effect—can still impact positioning accuracy. Remote sensing scientists capitalize on these positioning errors to infer atmospheric and Earth’s surface properties. This research area forms the discipline of GNSS remote sensing.5
However, GNSS remote sensing does not explicitly refer to GNSS hydrological remote sensing. It is broadly classified into two domains: GNSS atmospheric sensing and GNSS Earth surface sensing.
GNSS atmospheric sensing comprises ground atmospheric sounding techniques and/or the low Earth orbit (LEO) satellite-based GNSS radio occultation (GNSS-RO), which uses refracted signals traveling through the atmosphere. In this approach, a single antenna receives direct signals from GNSS satellites, employing observables such as pseudorange and carrier phase, similar to those used in positioning. Ground-based atmospheric sounding, first employed in 1992 to estimate PWV,6 was later augmented by spaceborne GNSS-RO missions, including the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC-1, 2006, and COSMIC-2, 2019). Recent advances extend these techniques to land surface property studies.7
GNSS Earth surface sensing exploits surface-reflected signals for ocean, land, and cryosphere monitoring. This domain includes the following:
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(1)
GNSS interferometric reflectometry (GNSS-IR) uses ground-based geodetic or low-cost instruments with a single antenna to measure the path delay between direct and reflected signals to infer surface height-related variables. It was first introduced in 2008 to measure soil moisture8 and then extended to snow depth (2009) and vegetation (2010) applications.
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(2)
GNSS reflectometry (GNSS-R) employs specialized receivers with high-gain antennas, often mounted on small LEO satellite constellations, to capture surface-reflected signals, enabling the measurement of surface heights and physical properties via carrier phase and signal power observables. Early work demonstrated the passive reflectometry and interferometric system (PARIS) concept in 1993,9 followed by satellite missions such as TechDemoSat-1, Cyclone GNSS (CyGNSS), BuFeng-1 A/B, FengYun-3 GNOS II, and the upcoming HydroGNSS.10
While hydrological studies are prominent in both domains, GNSS remote sensing also addresses broader, non-hydrological applications.
Conceptual framework of GNSS hydrology
Given the complexity of hydrological processes and the pressing need for accurate and reliable hydrological data to address challenges such as real-time monitoring, climate-driven variability, water resource sustainability, and extreme event prediction, explicitly integrating hydrological perspectives with GNSS methodologies is essential. However, the limitations of GNSS hydrogeodesy in only addressing hydrological applications, such as TWS change, coupled with the broader environmental scope of GNSS remote sensing, necessitate a new conceptual framework to precisely describe the emerging interdisciplinary field of GNSS + hydrology.”
If we set aside the aforementioned classification of application fields and focus on the GNSS signal and its interactions with hydrological components, three application dimensions can be identified: (1) positioning, which uses direct incident signals for geodetic measurements; (2) reflection, which exploits the surface-reflected signals to characterize hydrological properties; and (3) transmission, which analyzes signal refraction through media (troposphere, soil, or snowpack). We use the term “transmission” instead of “refraction” to distinguish it more clearly from “reflection.”
Therefore, we propose the formalization of GNSS hydrology, including three branches: GNSS-P ositioning hydrology, GNSS-Reflection hydrology, and GNSS Transmission hydrology, collectively referred to as GNSS-P.R.T. hydrology. Figure 1 outlines the key attributes of these branches, including their conceptual boundaries, observables, fundamental formulas, and target hydrological variables (a non-exhaustive list).
Figure 1.
Conceptual framework of GNSS hydrology
IR, interferometric reflectometry; R, reflectometry; RO, radio occultation; PWV, precipitable water vapor; SWE, snow water equivalent; LWC, liquid water content. The Meanings of some of the variables are as follows (due to page limits, please contact the author for a full list): , pseudorange measurement for the i satellite at time G; , carrier phase measurement for the i satellite at time G; SNR, signal-to-noise ratio; , power of the received reflected signal; , total path length of the reflected signal received; , change in the refractivity; , power of the refracted signal; , phase delay.
GNSS-Positioning hydrology
Positioning is defined as the process of measuring the coordinates of the GNSS antenna primarily for navigation and monitoring Earth’s deformation. This branch is basically the same as the well-known GNSS hydrogeodesy, with the primary observables being pseudorange and carrier phase. Geophysical applications focus on accurately measuring the vertical and horizontal position changes (displacements) due to various geophysical phenomena. These displacement data are closely linked to variations in terrestrial water storage, enabling researchers to infer total terrestrial water anomalies, groundwater storage changes, and seasonal effects, such as snowpack accumulation and melting. The mechanism behind this method often involves the elastic/poro-elastic deformation of Earth’s surface in response to changes in hydrological load, which directly translates into measurable surface displacement.
GNSS-Reflection hydrology
In this context, the definition of GNSS reflection is extended to encompass not only the forward-scattering signal traditionally associated with GNSS-R but also other scattering phenomena, such as GNSS-IR’s interferometric forward scattering and other potential backscattering. This extension is based on the premise that reflection underpins all scattering behaviors. The primary observable for GNSS-R altimetry is the carrier phase, while GNSS-R uses signal power, and GNSS-IR mainly uses the signal-to-noise ratio (SNR) for other applications. The emerging GNSS bistatic SAR (GNSS-SAR) system, which employs high-gain beamforming payloads, also belongs to this branch. However, it is in its infancy and has thus far been applied primarily for target detection. This technique can measure hydrological variables, including soil moisture, snow depth, flood extent, and dynamics of inland water bodies, significantly enriching observational capabilities for hydrological studies.
GNSS-Transmission hydrology
This field includes well-established atmospheric sounding, i.e., ground atmospheric sounding and spaceborne GNSS-RO, and the newly developed land surface applications using transmissive signals. The primary observables of atmospheric sounding are pseudorange and carrier phase. It converts the tropospheric path delay into variables like the PWV. Transmission through land requires two geodetic antennas: one on the ground (or underground) and the other as a reference positioned above the medium layer. The primary observables of this configuration include not only conventional pseudorange and carrier phases but also signal power attenuation. The fundamental mechanism involves measuring signal path delays and attenuation caused by interaction with media. It thus can provide detailed insights into subsurface hydrological processes, such as snowpack liquid water content, snow water equivalent (SWE), and soil moisture retention characteristics.
Discussions and outlook
We propose the GNSS-P.R.T. hydrology framework to establish a unified paradigm for GNSS hydrology. However, the boundaries between the three branches are not rigid, and they can be integrated to provide a more comprehensive understanding of the hydrological cycle. For instance, as shown in Figure 1, multiple GNSS techniques, such as GNSS-R and GNSS-Transmission, can measure the same variable (e.g., soil moisture) at different resolutions. Emerging commercial constellations, such as SPIRE and Tianmu-1, carry both GNSS-RO and GNSS-R payloads, have been used for operational applications. Additionally, geodetic GNSS receivers can contribute across all three branches. Notably, the proposed framework is based on the current state of GNSS technologies applied to hydrology, and it can be adjusted and expanded in the future as this field develops.
The GNSS hydrology field, taking advantage of its multiple spatial-temporal resolutions under multiple platforms, can work better in coordination with other hydrological research methods. Products of GNSS hydrology can be directly integrated into hydrological and land surface models through data assimilation techniques, significantly enhancing model initialization, calibration, and validation processes. Leveraging the multi-scale observations of GNSS—from ground based to spaceborne, complementing microwave radiometry and SAR—enables high-resolution hydrological datasets. The synergy of high-precision GNSS-derived hydrological variables with other hydrological observation methods enables improved spatial-temporal representation of key hydrological processes, facilitating more accurate predictions of water resource availability, flood risk, drought monitoring, and climate impact assessments. The reliability of GNSS-derived datasets is essential for such integrations and will drive further advancements in instrumentation and modeling.
Funding and acknowledgments
This work is jointly supported by the National Natural Science Foundation of China (NSFC) projects (grant no. 42471511), the Beijing Nova Program (grant nos. 20230484327 and 20240484540), the Hunan Provincial Natural Science Foundation project (grant no. 2024JJ9186), and the Fundamental Research Funds for the Central Universities, Peking University. M.A.K. was supported by 1311 DFG under SFB 1502/1-2022 (project number 450058266). We thank Wang Ma, Chengjia Liang, and Jie Zhang for their help with the collection and analyses of the published papers.
Declaration of interests
The authors declare no competing interests.
Published Online: June 13, 2025
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